The goal of business analytics is to turn large sets of raw data into meaningful and manageable information for business use. The problem is that there are many different ways to measure and analyze raw data. Furthermore, general business analytical outputs tend to relate to the “average customer,” which is not ideal as there are different types of customer/user experiences that are therefore left behind and not analyzed.

Often times a software provider will offer a variety of service levels on its platform (ie. free trial, basic, premium, etc.). An online retailer will have customers from different demographics, while a gaming platform will have gamers at different skill levels. By breaking up all of these users into different cohorts based on their similarities, a company can better understand how and why certain user cohorts behave the way they do.

For example: expert gamers, or cohort 1, will care more about advanced features and lag time compared to new sign-ups, or cohort 2. With these two cohorts determined and the analysis performed, a gaming company is presented with a visual representation of data which is specific only to them. It can then see that a slight lag in load times has been translating into a significant loss of revenue from advanced gamers, while new sign-ups have not even noticed the lag. Had the company simply looked at its overall revenue reports for all customers, it would not have been able to see the differences between these two cohorts.

Cohort analysis allows companies to pick up on patterns and trends so that necessary changes can be customized for relevant consumers only.